Lecture Notes for Stochastic Computing

  1. Introductory Lectures on Stochastic Computing
  2. Introductory Lectures on Monte Carlo Methods
  3. Early History of Monte Carlo
  4. Random Number Generation
  5. Testing Random Numbers
  6. Nonuniform Generation
  7. Direct Simulation
  8. General Principles of the Monte Carlo Method (variance reduction)
  9. Conditional Monte Carlo and Solving Linear Problems
  10. Monte Carlo Methods for Partial Differential Equations
  11. Deterministic Particle Methods
  12. Brownian Motion and Probability
  13. Stochastic Differential Equations (W. P. Petersen)